Meta Learning: How To Learn Deep Learning And Thrive In The Digital World
Tags: #technology #ai #deep learning #machine learning #programming #self-improvement #careers
Authors: Radek Osmulski
Overview
This book distills my experiences and insights from my journey into the world of deep learning. It’s a practical guide for those who want to learn quickly and effectively, especially those who, like me, didn’t start programming at a young age or have a traditional computer science background. It challenges conventional wisdom about the role of theory in learning, advocating for a practice-first approach. The book emphasizes hands-on experimentation, the importance of building projects, and leveraging the power of the deep learning community. It delves into the nuances of effective learning, covering topics such as debugging, tool mastery, and maintaining focus. I challenge readers to put their assumptions to the test, embrace new experiences, and find a mentor, even if it’s through online resources. I argue that sharing your work, even in its early stages, is crucial for accelerating your learning, establishing credibility, and connecting with the community. I reveal the secrets of effective machine learning project structure, emphasizing the importance of a good validation strategy, iterative development, and continuous experimentation. I debunk the myth that expensive hardware is essential, emphasizing the importance of efficient code and resource utilization. I offer practical advice on how to win at Kaggle competitions, using them as a platform for learning and skill development. This book guides you on how to navigate the deep learning community, with a particular focus on Twitter and online forums. I encourage readers to prioritize their mental well-being, manage their social media usage, and create mental space for deep work. My personal experiences with incorporating fitness and nutrition into my routine are also shared as examples of how seemingly unrelated habits can significantly impact your energy levels, focus, and overall well-being. This book is not just about technical skills but also about adopting a mindset of continuous learning, experimentation, and community engagement, ultimately empowering you to thrive in the ever-evolving world of deep learning.
Book Outline
1. From Not Being Able to Program to Deep Learning Expert
Becoming a true developer requires not only programming skills but also a deep understanding of tools and processes that contribute to effective problem solving in a development context. This chapter outlines the four key pillars that form a solid foundation for deep learning: basic programming knowledge, mastering a code editor, using version control (like git and GitHub), and gaining proficiency in using a computer for coding tasks, including understanding the file system and cloud environments. The fast.ai course, “Practical Deep Learning for Coders,” is recommended as a comprehensive resource for learning deep learning efficiently. It provides a unique approach that emphasizes practice and feedback loops.
Key concept: To become a proficient deep learning practitioner, you must first become a developer. This involves more than just programming; it’s about understanding the broader context of development, including how to use tools, navigate code effectively, and solve problems.
2. Theory vs Practice
Don’t get bogged down trying to master all the theory before diving into practice. Instead, embrace a cyclical approach of learning some theory, immediately applying it through hands-on practice, and then returning to theory with a deeper understanding gained from practical experience. This iterative process helps you learn faster and retain knowledge more effectively. It also creates crucial feedback loops that help identify blind spots and refine your understanding.
Key concept: For best effects, use one cup of theory, one cup of practice. Rinse and repeat.
3. Programming is About What You Have to Say
Programming is fundamentally about expressing ideas and solving problems through code. Don’t focus solely on mastering syntax and features of a language; instead, focus on using the language to build things and solve problems that interest you. Domain knowledge and the ability to express ideas effectively are far more valuable than deep knowledge of a language’s intricacies. Reading and writing code, even in the context of small, practical projects, is the most effective way to become a good programmer.
Key concept: The fastest way to learn to program is to learn how to say something useful.
4. The Secret of Good Developers
Good developers understand the value of deep focus and minimizing distractions. Context switching is costly in terms of time and energy, significantly hindering productivity. Long, uninterrupted work sessions allow for deeper immersion in tasks, promoting better problem-solving and creative output. Minimize distractions, put away your phone, and dedicate focused time blocks for optimal development work.
Key concept: If you want to make progress on learning or writing, be it text or code, I do not know of a better technique than long, uninterrupted sessions.
5. The Best Way to Improve as a Developer
Improving your development skills boils down to consistent practice through reading and writing code. Reading open-source code, even if it feels overwhelming initially, exposes you to different coding styles and problem-solving approaches. Building small, practical projects, even if they’re not complex, helps you apply your knowledge and learn through hands-on experience. Consistent practice, regardless of the initial quality of your output, is the key to becoming a proficient developer.
Key concept: The key to becoming a great developer is reading and writing a lot of code. The quality of the code you produce along the way doesn’t matter as long as you continue to read and write.
6. How to Use Your Tools to Achieve a State of Flow
Flow state, characterized by deep immersion and effortless productivity, can be achieved more consistently by optimizing your work environment and tools. Choosing tools that minimize distractions and interruptions, such as code editors that allow for keyboard-centric workflows, can help maintain focus. Planning, experimenting with toy datasets, and mastering your tools can minimize disruptions and contribute to a smoother workflow that fosters flow.
Key concept: Achieving a state of flow is not a given, but there are ways to make it more probable. We can organize our work to remove obstacles, so that it will flow from our hands.
7. Use Reality as Your Mirror
Don’t be afraid to challenge your assumptions and put your beliefs to the test. The real world often operates differently than we expect, and embracing new experiences, even if they seem daunting, can lead to valuable insights and opportunities. Be open to feedback from the world, and actively seek out situations that challenge your existing mental models. By doing so, you’ll gain a more accurate understanding of reality and be better equipped to navigate it.
Key concept: If you want to live a different life tomorrow than you are living today, you have to put your beliefs to the test. And going from not being a deep learning professional, to being one, might require questioning a lot of assumptions.
8. Do Genuine Work (It Compounds)
Don’t just think about doing things – take action! Genuine work, work that creates tangible outputs, compounds over time, leading to progress and growth. Take notes, write outlines, create drafts, build prototypes, and share your work with others. The act of externalizing your thoughts and efforts solidifies learning, invites feedback, and creates a foundation for further progress.
Key concept: Genuine work compounds. You think about something, even if you do so very deeply, and the next day you wake up and nearly nothing remains.
9. The Hidden Game of Machine Learning
The ability to generalize to unseen data is at the heart of machine learning. Understanding how to create a robust validation strategy is fundamental to building effective machine learning models. A good validation set allows you to assess your model’s performance on unseen data, ensuring it can effectively generalize beyond the training data and provide reliable predictions in real-world scenarios.
Key concept: The best starting point for gaining a deeper understanding of the ability to generalize to unseen data is a blog post by Rachel Thomas, How (and why) to create a good validation set.
10. How to Structure a Machine Learning Project
This chapter emphasizes the importance of a structured approach to machine learning projects. Start by establishing a robust validation strategy, followed by creating a simple baseline model as a starting point. From there, iteratively improve your solution, focusing on one component at a time and validating your work at each step. This incremental approach helps you maintain sanity in complex projects and facilitates consistent progress.
Key concept: “The only way to maintain your sanity in the long run is to be paranoid in the short run.”
11. How to Win at Kaggle
Participating in Kaggle competitions offers an invaluable learning experience. Start by joining a competition early, as setting up, understanding the data, and building a basic pipeline can take time. Winning at Kaggle requires more than just cookie-cutter solutions; it demands creativity, experimentation, and continuous learning. Engage with the Kaggle forums to learn from others’ experiences, gain inspiration, and stay up-to-date with the latest techniques. Remember, the real prize is the knowledge and experience you gain, not just the leaderboard position.
Key concept: The first step to winning at Kaggle is to join a competition early.
12. The Best Hardware for Deep Learning
Don’t get caught up in endless hardware debates; focus on effectively utilizing what you have. A good starting point is to use cloud services like AWS or GCP to experiment and learn before investing in a home rig. If deep learning is for you, a home rig with a decent GPU is generally the most cost-effective option. Ultimately, it’s not about the hardware itself but how you use it. Learning to optimize code for efficient GPU utilization is key to maximizing your hardware’s potential.
Key concept: The advice here is to get the biggest GPU [in terms of RAM] you can afford.
13. Debugging with Ease is a Superpower
Debugging effectively is crucial in machine learning due to the complexity of the code and the often subtle nature of bugs. Leverage the interactive nature of Jupyter Notebooks to aid debugging. Use debugging tools and techniques to pinpoint errors, inspect variable values, and experiment with fixes in real-time. This approach makes debugging less daunting and accelerates the process of finding and resolving issues in your code.
Key concept: Machine learning code is notoriously hard to write. It is extremely easy to introduce subtle bugs. Our code might still run and we might get a decent result, but it won’t be as good as it could otherwise be.
14. Time Yourself
Timing your code using tools like the %%timeit cell magic in Jupyter Notebooks can reveal unexpected performance bottlenecks. Small optimizations, especially in data loading and preprocessing, can accumulate significant time savings over many iterations. This focus on efficiency ensures you’re making the most of your computational resources, especially your GPU, and accelerating the training process.
Key concept: Timing your code is a very useful technique across the board, but especially when you are constructing datasets and dataloaders.
15. You Can’t Learn a Profession by Studying a Textbook
Don’t prioritize theoretical knowledge over practical application. While theory is important, focusing solely on it without applying your knowledge to real-world problems leads to an incomplete understanding. Deep learning requires a balance of theoretical foundation and practical experience. Embrace practical projects and hands-on experimentation to solidify your understanding and develop a well-rounded skill set.
Key concept: It doesn’t make sense to invest all of your time into learning calligraphy if you have nothing to write about! Likewise, it doesn’t make a lot of sense to work on building a table but focus all your effort on a single leg and then complain that the table will not stand!
16. On Finding a Job
Finding a job in deep learning requires more than just technical skills. Building credibility by showcasing your work and engaging with the community is essential. Network with others, contribute to open-source projects, share your knowledge through blog posts or online forums, and actively seek out opportunities to demonstrate your capabilities. This approach not only helps you connect with potential employers but also establishes you as a knowledgeable and valuable contributor in the field.
Key concept: Employability is one of the hardest subjects to talk about. There is simply no straightforward answer.
17. The Deep Learning Party is on Twitter
Engage with the deep learning community on Twitter. It’s a valuable platform for learning from leading researchers, staying up-to-date with the latest advancements, and connecting with like-minded individuals. However, be mindful of the potential downsides of social media, such as distractions and information overload. Manage your Twitter usage by disabling algorithmic timelines and notifications, limiting your time spent on the platform, and prioritizing your mental well-being.
Key concept: Twitter is where the deep learning community hangs out.
18. Share Your Work
Share your work publicly to establish credibility, connect with the community, and accelerate your learning. By putting your work out there, you invite feedback, attract opportunities for collaboration, and demonstrate your capabilities to potential employers. The act of sharing also forces you to organize your thoughts, reflect on your work, and improve your communication skills.
Key concept: The actions that you perform in public weave a net around you. Whenever you tweet, publish a blog post, or share a GitHub repository, you add a new segment that starts transmitting your credibility into the world.
19. I am Scared to Share My Work! Help!
Don’t let fear hold you back from sharing your work. While it can be intimidating to put yourself out there, the benefits far outweigh the risks. The most likely negative outcome is simply that people won’t engage with your work, which is a far cry from the catastrophic consequences we often imagine. If you’re not happy with something you’ve shared, simply delete it – no one will notice or mind.
Key concept: Do whatever you need to do to make not only the actual costs of experimentation low but the emotional costs low as well.
20. What to Focus on in Sharing Your Work
When sharing your work, focus on your personal experience and the lessons you’ve learned. Don’t feel pressured to speak on topics outside your expertise. By sharing your authentic journey, you provide valuable insights to others who might be facing similar challenges, create a relatable connection with your audience, and establish yourself as a credible and trustworthy source of information.
Key concept: Speak to your experience. This is one of the most valuable things that we all have to offer.
21. Don’t Lose Sight of What is Important
Don’t get caught up in chasing vanity metrics on social media. While building a large following can have its benefits, it’s not essential for success in deep learning. Focus on learning and sharing your work with the community, and let the growth of your following happen organically as a byproduct of your valuable contributions. Remember, the goal is to learn and advance your skills, not to become a social media influencer.
Key concept: You do not need to make it big on social media to get your first Deep Learning job. You do not need to have followers to take advantage of learning in public.
22. Make Mental Space for What Matters
Protect your mental space by minimizing distractions and prioritizing activities that contribute to your well-being. Social media can be a significant source of distraction and negativity, draining your energy and hindering your ability to focus on what’s important. Curate your online experience, limit your time on social media, and create mental space for creative work and meaningful pursuits.
Key concept: “Peace of mind is the most important prerequisite for creative work.”
23. To Engage Afterburners, Find a Mentor
Finding a mentor can significantly accelerate your learning. Mentorship doesn’t require a formal relationship; it can take many forms, including learning from courses, books, online communities, or even social media posts. Actively seek out individuals who are experts in areas you want to learn, and engage with their work through reading, listening, and participating in discussions. Be open to learning from a variety of sources, and remember that mentorship can come from unexpected places.
Key concept: Mentors may communicate via books or lectures, GitHub issues, or tweets. All it takes is for us to do the work, to look through the material pertinent to our situation.
24. The Biggest Regret of Fast.ai Students
The most common regret among fast.ai students is not dedicating enough time to practice and hands-on experimentation. While theory is important, practical experience is crucial for solidifying your understanding and developing the skills necessary to become a proficient deep learning practitioner. Prioritize doing over passive learning, and embrace experimentation as a core part of your learning process.
Key concept: “I wished I spent more time coding and experimenting and less time studying in a more traditional sense.”
25. Persistence is Everything
Persistence is key to success in deep learning. The journey can be challenging, and you’ll inevitably encounter obstacles and setbacks. However, consistent effort, combined with active involvement in the community, will lead to significant progress over time. Learning compounds, so even small efforts made consistently can accumulate substantial results in the long run.
Key concept: Combine persistence with community involvement and you cannot be stopped.
26. Change Is About What Not To Do
Changing your approach to learning often requires not just adding new activities but also letting go of old habits that are no longer serving you. Identify behaviors that are hindering your progress and consciously replace them with more effective strategies. This might involve reducing time spent on passive learning and increasing time dedicated to practice and experimentation.
Key concept: Or, to put it in other words, to welcome the new, we need to say goodbye to the old.
27. Learning Might Just Be Enough
Learning is fundamental to success in deep learning, and even focusing solely on learning can lead to positive outcomes. However, to maximize your learning, adopt a strategy of continuous evaluation and adaptation. Regularly assess whether your learning approach is yielding the desired results, and be willing to change course if it’s not.
Key concept: Even if you focus solely on learning, whatever learning means to you, there is a great chance you will be alright.
28. More Perspectives on Mentoring
This chapter provides additional perspectives on mentorship, emphasizing the importance of reciprocity and mutual support. It highlights the concept of “Permissionless Apprenticeship,” where aspiring learners gain mentorship by first providing value to potential mentors through contributions to their work or projects. This approach fosters a collaborative learning environment and demonstrates initiative and commitment to learning. The chapter also encourages experienced practitioners to actively mentor others, highlighting the positive impact it can have on both the mentee and the mentor.
Key concept: Mentorship doesn’t have to be a one-way street. By actively promoting and supporting others, you not only contribute to the growth of the community but also deepen your own understanding and create valuable connections.
29. Tap Into the Power of the Community to Learn Faster
This chapter emphasizes the power of community in accelerating learning. Being part of a welcoming and supportive community provides access to diverse perspectives, exposes you to best practices, and offers opportunities for collaboration and mutual support. Engaging with others who are on a similar learning journey can be both motivating and insightful, helping you overcome obstacles, gain inspiration, and progress faster than you would in isolation.
Key concept: We learn the fastest when we are part of a community of learners.
30. Energize
This chapter shares personal experiences and insights on how to energize your learning journey. It emphasizes the importance of taking care of your physical and mental well-being to maintain optimal energy levels and focus. It explores the benefits of incorporating regular physical activity, such as yoga, into your routine and adopting a conscious approach to nutrition, such as time-restricted eating and reducing sugar intake. These practices can not only improve your overall health but also enhance your mental clarity, focus, and energy levels, supporting your deep learning pursuits.
Key concept: The most empowering experience in my deep learning journey has been the discovery of fast.ai courses. What I outline below has the potential to match it.
Essential Questions
1. What is the most effective approach to learning and applying deep learning according to the author?
This book advocates for a practice-oriented approach to learning deep learning. Starting with a simple baseline and iteratively improving it through experimentation, focusing on one component at a time, and validating the work at each step. This allows for faster learning, better understanding of the problem domain, and consistent progress towards a robust solution.
2. What are the key elements beyond technical skills that contribute to becoming a successful deep learning practitioner?
The author stresses that mastering the technical aspects of deep learning is only one part of becoming a successful practitioner. Building a strong foundation as a developer, which includes mastering tools, effective code navigation, and a problem-solving mindset is crucial. Equally important is engaging with the deep learning community, sharing your work, finding mentors, and prioritizing continuous learning and personal growth. This holistic approach ensures that you not only possess the technical skills but also the meta-skills needed to thrive in the dynamic world of deep learning.
3. What role does community play in learning deep learning effectively?
This book emphasizes the importance of community engagement in the deep learning journey. It advocates for actively participating in online forums like Kaggle and fast.ai, and using social media platforms like Twitter to connect with other practitioners. The author argues that by sharing your work, asking questions, and learning from others’ experiences, you can accelerate your learning, gain valuable insights, and build credibility within the community. It also highlights the importance of finding a mentor, either through direct interaction or by learning from their publicly available work.
4. How does the author reconcile the role of theory and practice in deep learning?
The book argues that while theoretical knowledge is important, it should not be prioritized over hands-on practice. The author, drawing on personal experience and insights from prominent figures in the field, emphasizes that practical experience provides essential feedback loops, exposes blind spots in understanding, and often precedes and informs the development of theory. It encourages readers to adopt a practice-first approach, using theory to support and enhance their understanding gained through practical work.
5. What is the author’s perspective on the role of social media, specifically regarding pursuing vanity metrics vs. focusing on learning?
The author, while acknowledging the potential benefits of building a large social media following, argues that it should not be the primary focus for aspiring deep learners. He cautions against getting caught up in the pursuit of vanity metrics, emphasizing that the primary goal should be to learn, build projects, and share your work with the community. He advocates for using social media as a tool for learning and connecting with other practitioners, but not letting it distract from the core pursuits of deep learning.
Key Takeaways
1. Embrace a Practice-First Approach with Iterative Improvement
Starting with a simple, functional model allows for quicker feedback, better understanding of the problem domain, and gradual integration of more complex techniques as you progress. Don’t get bogged down trying to build the most sophisticated solution from the outset; instead, embrace an iterative approach, building upon a solid foundation.
Practical Application:
An AI product engineer can apply this by starting with a simple chatbot using a pre-trained model and basic rule-based responses. As they gain experience and learn more techniques, they can iteratively improve the chatbot, incorporating more sophisticated natural language processing models and refining its conversational abilities.
2. Share Your Work Early and Often to Accelerate Learning
Sharing your work not only establishes credibility and helps you connect with the community, but it also creates valuable feedback loops that can significantly accelerate your learning and improve the quality of your work. The act of explaining your work to others forces you to organize your thoughts and deepen your understanding.
Practical Application:
An AI product engineer working on a recommendation system can start by sharing their initial approach and results with their team, even if it’s just a simple collaborative filtering model. The feedback they receive can help them identify blind spots, explore new ideas, and improve the system iteratively.
3. Master Debugging Techniques Within Jupyter Notebooks
Jupyter Notebooks offer a powerful environment for interactive development and debugging. By mastering debugging tools and techniques within Jupyter, you can efficiently diagnose and resolve issues, minimizing frustration and accelerating your development process. This is particularly valuable in machine learning, where code complexity can make traditional debugging methods challenging.
Practical Application:
An AI product engineer struggling with a bug in their code can leverage the interactive nature of Jupyter Notebooks to pinpoint the error by using debugging tools, inspecting variable values at the point of failure, and experimenting with modifications in real-time to test potential fixes.
4. Start with Simple Models, Validate, and Then Scale Up
Starting with simpler models provides a valuable reference point for evaluating more complex approaches. It helps identify potential issues early on, provides a better understanding of the problem domain, and allows for more controlled experimentation before scaling up to more complex models. This approach saves time and resources by ensuring you’re on the right track before diving into complex implementations.
Practical Application:
If an AI product engineer is working on a complex computer vision project, instead of immediately building a large, complex neural network, they could experiment with simpler models like Random Forests or basic convolutional networks to gain insights into the data and validate their approach before investing time in more complex solutions.
5. Actively Engage with the Deep Learning Community
Learning in isolation can be challenging and slow. The deep learning community is full of passionate and helpful individuals willing to share their knowledge and support others. Engage with the community by actively participating in online forums, attending online meetups, and connecting with other practitioners on social media.
Practical Application:
An AI product engineer can leverage online forums like the fast.ai forums, Kaggle discussions, or communities on platforms like Reddit and Discord to connect with other practitioners, ask questions, share their work, and learn from others’ experiences. This can provide valuable insights, accelerate learning, and offer support and motivation throughout their journey.
Suggested Deep Dive
Chapter: How to Structure a Machine Learning Project
This chapter dives deep into the practicalities of managing machine learning projects, offering a structured approach that can be directly applied to real-world scenarios. It’s particularly relevant for AI product engineers as it provides a framework for building robust, reliable, and scalable machine learning solutions.
Memorable Quotes
How to Read This Book. 13
“You can’t connect the dots looking forward. You can only connect them looking backwards”
The Problem With Theory. 27
“I have not failed. I’ve just found 10,000 ways that won’t work.”
Practice Wins Every Time. 34
“There’s just a way of learning that applies to most domains, and once you sort of find that to be successful in one domain, it’s not that hard to just basically say ‘copy paste’.”
The Best Way To Improve as a Developer. 44
“You don’t sharpen your skills with resources, books, or articles. You sharpen your skills with practice. If you want to get better, go do the thing.”
How to Structure a Machine Learning Project. 59
“The only way to maintain your sanity in the long run is to be paranoid in the short run.”
Comparative Analysis
While many books focus on the technical aspects of deep learning, “Meta Learning” distinguishes itself by focusing on the meta-skills needed to thrive in the field. Unlike traditional machine learning textbooks that emphasize theory, this book, much like Jeremy Howard’s “Deep Learning for Coders with Fastai and PyTorch,” champions a practice-first approach, aligning with the philosophy of learning by doing. However, Osmulski goes a step further by exploring the broader context of learning and professional development, emphasizing the importance of community engagement, personal branding, and mental well-being. This echoes Cal Newport’s “Deep Work” in its emphasis on focused work and minimizing distractions, but applies these principles specifically to the context of deep learning. The book’s unique contribution lies in its holistic approach, integrating technical learning with personal growth and professional development strategies, making it a valuable resource for aspiring deep learners seeking a comprehensive guide to navigate the field.
Reflection
Meta Learning offers a refreshing and practical perspective on how to succeed in the field of deep learning. The book’s strength lies in its emphasis on practice, iterative development, community involvement, and personal growth. While the author’s emphasis on practice over theory might seem to downplay the importance of a strong theoretical foundation, it’s more about finding the right balance and not getting bogged down in theory before gaining practical experience. A skeptical reader might question the author’s focus on Kaggle competitions as the primary avenue for learning and skill development, as not all deep learning work aligns with the competitive nature of Kaggle. However, the book’s broader message of continuous learning, experimentation, and community engagement remains valuable regardless of the specific context. Overall, “Meta Learning” is a valuable resource for anyone looking to navigate the world of deep learning, offering practical advice, insightful perspectives, and a unique blend of technical and personal development strategies.
Flashcards
Why is establishing a baseline crucial in a machine learning project?
It provides insights into what is possible, helps identify unknown aspects of the problem, and serves as a reference for evaluating future iterations.
What are some important considerations for creating a validation split in a machine learning project?
Random sampling might not be enough. Consider stratified splits based on classes, time-based splits for temporal data, or other considerations specific to the problem domain.
Why is it beneficial to join a Kaggle competition early?
Joining early allows for ample time to understand the data, experiment with different models, and leverage the Kaggle forums for learning and inspiration.
How does reading open-source code benefit a developer?
Reading code, even if it’s initially overwhelming, exposes you to different coding styles, problem-solving approaches, and best practices.
What is an effective way to practice coding and improve as a developer?
Focus on building small, practical projects, even if they’re not complex. The act of building reinforces learning and provides valuable hands-on experience.
Why are long, uninterrupted work sessions beneficial for developers?
It minimizes distractions, allows for deeper immersion in tasks, and promotes better problem-solving and creative output.
What is ensembling in machine learning?
It’s a technique where you combine predictions from multiple models to reduce the impact of uncorrelated errors, leading to improved overall performance.
What is a primary benefit of time-restricted eating?
It allows your body to rest from digesting, restores hormonal balance, and helps regulate insulin levels.